Papers with CDTB
A Unified RvNN Framework for End-to-End Chinese Discourse Parsing (C18-2)
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| Challenge: | Existing work on Chinese discourse parser relies on external packages to extract linguistic features from free text. |
| Approach: | They propose an end-to-end Chinese discourse parser based on recursive neural network to jointly model the subtasks including elementary discourse unit segmentation, tree structure construction, center labeling, and sense labeling. |
| Outcome: | The proposed framework achieves state-of-the-art in the Chinese Discourse Treebank dataset. |
Discourse Parsing Enhanced by Discourse Dependence Perception (2022.aacl-main)
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| Challenge: | Top-down neural models still suffer from the top-down error propagation issue . previous studies gradually switch from feature-based machine learning methods to deep neural models . |
| Approach: | They propose a top-down framework that learns from discourse dependency and constituency parsing through one shared encoder and two independent decoders. |
| Outcome: | The proposed framework learns from discourse dependency and constituency parsing through one shared encoder and two independent decoders on a Chinese discourse corpus. |
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese (P19-1)
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| Challenge: | Currently, most studies on implicit discourse relation recognition use sentence-level representations . Chinese is a paratactic language that tends to pro-drop clause connectives . |
| Approach: | They propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. |
| Outcome: | The proposed model outperforms state-of-the-art models in micro and macro F1 scores on a Chinese discourse corpus. |
Learning Dynamic Representations for Discourse Dependency Parsing (2023.findings-emnlp)
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| Challenge: | Existing models characterize transition states by examining a certain number of elementary discourse units (EDUs) Existing work neglects the arcs obtained from the transition history. |
| Approach: | They propose to employ GAT-based encoder to learn dynamic representations for sub-trees constructed in previous transition steps. |
| Outcome: | The proposed model retains access to parsed EDUs through the obtained arcs, especially when handling lengthy text spans with complex structures. |